DETAILED ACTION
This Office Action is in response for Continuation Application # 19/070,350 filed on March 04, 2025 in which claims 1-16 are presented for examination.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of claims
Claims 1-16 are pending, of which claims 1-16 are rejected under 35 U.S.C. 103 and also claims 1-16 are rejected under 35 U.S.C. 101.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-16 are rejected under 35 U.S.C. 101. because the claims are directed to an abstract idea; and because the claims as a whole, considering all claim elements both individually and in combination, do not amount to significantly more than the abstract idea, see Alice Corporation Pty. Ltd. v. CLS Bank International, et al, 573 U.S. (2014). In determining whether the claims are subject matter eligible, the Examiner applies the 2019 USPTO Patent Eligibility Guidelines. (2019 Revised Patent Subject Matter Eligibility Guidance, 84 Fed. Reg. 50, Jan. 7, 2019.)
Step 1: Is the claim to a process, machine, manufacture, or composition of matter? Yes—Claims 1-16 recite a method, device and readable medium respectively.
The analysis of claims 1, 15 and 16 is as follows:
Step 2A, prong one: Does claims 1, 15 and 16 recite an abstract idea, law of nature or natural phenomenon? Yes—the limitations of “claim 1, receiving a user input selecting one or more data fields displayed in a user interface via the display generation component, wherein the one or more data fields are from a data source;
generating, based on a type of analysis selected from a plurality of selectable types of analysis and the one or more user-selected data fields, a plurality of candidate data visualizations; and
displaying, in the user interface via the display generation component, a subset of the plurality of candidate data visualizations as a collection that is associated with the type of analysis.
Claims 15 and 16, receive a user input selecting one or more data fields displayed in a user interface via the display generation component, wherein the one or more data fields are from a data source;
determine, based on the one or more user-selected data fields, a type of analysis from a plurality of selectable types of analysis;
generate, based on the type of analysis and the one or more user-selected data fields, a plurality of candidate data visualizations;
display, in the user interface via the display generation component, a subset of the plurality of candidate data visualizations as a collection that is associated with the type of analysis;
detect a user input selecting a data visualization of the subset of the plurality of candidate data visualizations; and
in response to the user input selecting the data visualization, add the selected data visualization to a dashboard of at least one visualization, wherein the dashboard is displayed in the user interface via display generation component” as drafted, are mental steps based on various processes can be performed in a human mind of display data visualization to a dashboard (acts of thinking, decision making). These limitations, therefore fall within the human mind processes group and with a pen & paper.
Step 2A, prong two: Does the claim recite additional elements that integrate the judicial exception into a practical application? No—the judicial exception is not integrated into a practical application as just stated as related to the technical field of computer science . Although the claim recites that the recited functionality includes “method”, “computer” and “readable medium”, these computer components are recited at a high-level of generality such that it amounts to no more than a mere instructions to apply the exception using generic computer component. In addition, the claim recites “claim 1, receiving a user input selecting one or more data fields displayed in a user interface via the display generation component, wherein the one or more data fields are from a data source;
generating, based on a type of analysis selected from a plurality of selectable types of analysis and the one or more user-selected data fields, a plurality of candidate data visualizations; and
displaying, in the user interface via the display generation component, a subset of the plurality of candidate data visualizations as a collection that is associated with the type of analysis.
Claims 15 and 16, receive a user input selecting one or more data fields displayed in a user interface via the display generation component, wherein the one or more data fields are from a data source;
determine, based on the one or more user-selected data fields, a type of analysis from a plurality of selectable types of analysis;
generate, based on the type of analysis and the one or more user-selected data fields, a plurality of candidate data visualizations;
display, in the user interface via the display generation component, a subset of the plurality of candidate data visualizations as a collection that is associated with the type of analysis;
detect a user input selecting a data visualization of the subset of the plurality of candidate data visualizations; and
in response to the user input selecting the data visualization, add the selected data visualization to a dashboard of at least one visualization, wherein the dashboard is displayed in the user interface via display generation component” are mere gathering data and applying process steps (i.e., displaying data); the computers that perform those functions and the mental steps are recited at a high level of generality that do not impose a meaningful limitation on the judicial exception and are insufficient to integrate the mental steps into a practical application. Although the claim recites the additional functionality “a subset of the plurality of candidate data visualizations“, the gathering and determining are also recited at a high level of generality and merely generally link to respective technological environments (e.g., visualization model) and therefore likewise amounts to no more than a mere instructions to apply the exception using generic computer components and is insufficient to integrate the steps into a practical application.
Step 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception? No— The recitation in the preamble is insufficient to transform a judicial exception to a patentable invention because the preamble elements are recited at a high level of generality that simply links to a field of use, see MPEP 2106.05(h). The claimed extra-solution of operation based on generating cubes from collection of queries is acknowledged to be well-understood, routine, conventional activity (see, e.g., court recognized WURC examples in MPEP 2106.05(d)(II)(i). Similarly, the gathering and generating are also recited at a high level of generality and merely generally link to respective technological environments. The claim thus recites computing components only at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using generic computer components. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept.
Taken alone, their additional elements do not amount to significantly more than the above- identified judicial exception (the abstract idea). Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation.
For the reasons above, claims 1, 15 and 16 are rejected as being directed to non-patentable subject matter under §101.
The analysis of claims 2-14 are as follows:
Step 2A, prong one: Does claims 2-14 recite an abstract idea, law of nature or natural phenomenon? Yes—the limitations of “Claim 2, wherein the user interface includes a plurality of selectable user interface elements corresponding to the plurality of types of analysis, and the method includes: detecting a user input selecting a respective user interface element of the plurality of user interface elements, wherein the respective user interface element corresponds to the type of analysis.
Claim 3, wherein the type of analysis is automatically determined based on the one or more user-selected data fields.
Claim 4, wherein the plurality of selectable types of analysis include at least one of a group consisting of measure analysis, change analysis, category analysis, and distribution analysis.
Claim 5, detecting a user input selecting a data visualization of the subset of the plurality of candidate data visualizations; and in response to the user input selecting the data visualization, adding the selected data visualization to a dashboard of at least one visualization, wherein the dashboard is displayed in the user interface via display generation component.
Claim 6, wherein the plurality of candidate data visualizations are generated based on additional data fields other than the one or more user-selected data fields.
Claim 7, wherein the additional data fields other than the one or more user- selected data fields are determined based on statistical analysis of the additional data fields meeting a variance threshold.
Claim 8, wherein the plurality of candidate data visualizations include values from at least one of the one or more user-selected data fields.
Claim 9, wherein the plurality of candidate data visualizations are generated via a predictive model that associates the one or more user-selected data fields to the type of analysis.
Claim 10, wherein: the one or more user-selected data fields are selected from a plurality of data fields in the data source; and a respective data field of the plurality of data fields in the data source includes a respective type, including a quantitative measure, a categorical field, a temporal field, or a geographic field.
Claim 11, further comprising: determining, based on the types of the one or more user-selected data fields, a secondary type of analysis from a plurality of selectable types of secondary analysis, wherein each secondary type of analysis is based on a combination of types of data attributes; and ranking, based on the secondary type of analysis, the plurality of candidate data visualizations.
Claim 12, wherein: the plurality of candidate data visualizations are associated with respective scores; the respective scores are based on the one or more user-selected data fields, the respective type of analysis, or the respective secondary type of analysis; and displaying the subset of the plurality of candidate data visualizations is based on the respective scores.
Claim 13, further comprising: updating respective ranking of the subset of the plurality of candidate data visualizations based on the user selection of the subset of the subset of the plurality of candidate data visualizations.
Claim 14, further comprising: updating respective ranking of the one or more data visualizations in the respective collection of the one or more collections based on the user selection of the subset of the one or more data visualizations in the respective collection of the one or more collections” as drafted, are mental steps based on various processes can be performed in a human mind of display data visualization to a dashboard (acts of thinking, decision making). These limitations, therefore fall within the human mind processes group and with a pen & paper.
Step 2A, prong two: Does the claim recite additional elements that integrate the judicial exception into a practical application? No—the judicial exception is not integrated into a practical application as just stated as related to the technical field of computer science . Although the claim recites that the recited functionality includes “method”, “computer” and “readable medium”, these computer components are recited at a high-level of generality such that it amounts to no more than a mere instructions to apply the exception using generic computer component. In addition, the claim recites “Claim 2, wherein the user interface includes a plurality of selectable user interface elements corresponding to the plurality of types of analysis, and the method includes: detecting a user input selecting a respective user interface element of the plurality of user interface elements, wherein the respective user interface element corresponds to the type of analysis.
Claim 3, wherein the type of analysis is automatically determined based on the one or more user-selected data fields.
Claim 4, wherein the plurality of selectable types of analysis include at least one of a group consisting of measure analysis, change analysis, category analysis, and distribution analysis.
Claim 5, detecting a user input selecting a data visualization of the subset of the plurality of candidate data visualizations; and in response to the user input selecting the data visualization, adding the selected data visualization to a dashboard of at least one visualization, wherein the dashboard is displayed in the user interface via display generation component.
Claim 6, wherein the plurality of candidate data visualizations are generated based on additional data fields other than the one or more user-selected data fields.
Claim 7, wherein the additional data fields other than the one or more user- selected data fields are determined based on statistical analysis of the additional data fields meeting a variance threshold.
Claim 8, wherein the plurality of candidate data visualizations include values from at least one of the one or more user-selected data fields.
Claim 9, wherein the plurality of candidate data visualizations are generated via a predictive model that associates the one or more user-selected data fields to the type of analysis.
Claim 10, wherein: the one or more user-selected data fields are selected from a plurality of data fields in the data source; and a respective data field of the plurality of data fields in the data source includes a respective type, including a quantitative measure, a categorical field, a temporal field, or a geographic field.
Claim 11, further comprising: determining, based on the types of the one or more user-selected data fields, a secondary type of analysis from a plurality of selectable types of secondary analysis, wherein each secondary type of analysis is based on a combination of types of data attributes; and ranking, based on the secondary type of analysis, the plurality of candidate data visualizations.
Claim 12, wherein: the plurality of candidate data visualizations are associated with respective scores; the respective scores are based on the one or more user-selected data fields, the respective type of analysis, or the respective secondary type of analysis; and displaying the subset of the plurality of candidate data visualizations is based on the respective scores.
Claim 13, further comprising: updating respective ranking of the subset of the plurality of candidate data visualizations based on the user selection of the subset of the subset of the plurality of candidate data visualizations.
Claim 14, further comprising: updating respective ranking of the one or more data visualizations in the respective collection of the one or more collections based on the user selection of the subset of the one or more data visualizations in the respective collection of the one or more collections” are mere gathering data and applying process steps (i.e., displaying data); the computers that perform those functions and the mental steps are recited at a high level of generality that do not impose a meaningful limitation on the judicial exception and are insufficient to integrate the mental steps into a practical application. Although the claim recites the additional functionality “a subset of the plurality of candidate data visualizations“, the gathering and determining are also recited at a high level of generality and merely generally link to respective technological environments (e.g., visualization model) and therefore likewise amounts to no more than a mere instructions to apply the exception using generic computer components and is insufficient to integrate the steps into a practical application.
Step 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception? No— The recitation in the preamble is insufficient to transform a judicial exception to a patentable invention because the preamble elements are recited at a high level of generality that simply links to a field of use, see MPEP 2106.05(h). The claimed extra-solution of operation based on generating cubes from collection of queries is acknowledged to be well-understood, routine, conventional activity (see, e.g., court recognized WURC examples in MPEP 2106.05(d)(II)(i). Similarly, the gathering and generating are also recited at a high level of generality and merely generally link to respective technological environments. The claim thus recites computing components only at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using generic computer components. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept.
Taken alone, their additional elements do not amount to significantly more than the above- identified judicial exception (the abstract idea). Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation.
For the reasons above, claims 2-14 are rejected as being directed to non-patentable subject matter under §101.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-16 are rejected under 35 U.S.C. 103 as being unpatentable over Bruno M. Fernandez-Ruiz US 2015/0331575 A1 (hereinafter ‘Fernandez’) in view of Anand et al. US 2015/0278214 A1 (hereinafter ‘Anand’).
As per claim 1, Fernandez disclose, A method for generating a collection of data visualization for dashboard composition (Fernandez: paragraph 0045: disclose most desirable content is always displayed with the best visual effect ‘visualization dashboard’ and the supplemental content for the most desirable content is dynamically refreshed accordingly), comprising:
at a computer system in communication with a display generation component (Fernandez: paragraph 0046: disclose the display layouts (e.g., direction of the content presentation) of the main content display panel and each supplemental content display panel), one or more processors, and memory storing one or more programs, wherein the one or more programs are configured to be executed by the one or more processors (Fernandez: paragraph 0073: disclose one or more processors, for executing program instructions), the one or more programs including instructions for (Fernandez: paragraph 0073: disclose program instructions to be executed by the CPU):
receiving a user input selecting one or more data fields displayed in a user interface (Fernandez: paragraph 0054: disclose user viewing interface receives user input from the user and transmits different types of user input to the user interface configuration module and navigation module, respectively. For example, the user input may include input regarding the configuration parameters of the user viewing interface in which the 3D viewing construct is to the rendered);
generating, based on a type of analysis selected from a plurality of selectable types of analysis and the one or more user-selected data fields, a plurality of candidate data visualizations (Fernandez: paragraph 0046 & Paragraph 0047: Fig. 4 and Fig 5: disclose two examples of rendering a 3D viewing ‘candidate data visualizations’ construct in a user viewing interface); and
displaying, in the user interface via the display generation component, a subset of the plurality of candidate data visualizations as a collection that is associated with the type of analysis (Fernandez: paragraph 0046 & Fig. 4: disclose a user interaction, such as rotating the display of the user device, may cause the re-rendering of the same 3D viewing construct in an updated user viewing interface. Examiner equates this teaching to subset of the plurality of candidate data visualizations).
It is noted, however, Fernandez did not specifically detail the aspects of
via the display generation component, wherein the one or more data fields are from a data source as recited in claim 1.
On the other hand, Anand achieved the aforementioned limitations by providing mechanisms of
via the display generation component, wherein the one or more data fields are from a data source (Anand: paragraph 0122: disclose user selects a set of data fields 402 from the data source(s)).
Fernandez and Anand are analogous art because they are from the “same field of endeavor” and both from the same “problem-solving area”. Namely, they are both from the field of “Content Visualization Systems”.
It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to combine the systems of Fernandez and Anand because they are both directed to content visualization systems and both are from the same field of endeavor. The skilled person would therefore regard it as a normal option to include the restriction features of Anand with the method described by Fernandez in order to solve the problem posed.
The motivation for doing so would have been to Information visualization uses visual representations of data to aid in human understanding of relationships and patterns in the data (Anand: paragraph 0002).
Therefore, it would have been obvious to combine Anand with Fernandez to obtain the invention as specified in instant claim 1.
As per claim 2, most of the limitations of this claim have been noted in the rejection of claim 1 above. In addition, Fernandez disclose, wherein the user interface includes a plurality of selectable user interface elements corresponding to the plurality of types of analysis, and the method includes: detecting a user input selecting a respective user interface element of the plurality of user interface elements, wherein the respective user interface element corresponds to the type of analysis (Fernandez: paragraph 0054: disclose user viewing interface receives user input from the user and transmits different types of user input to the user interface configuration module and navigation module, respectively. For example, the user input may include input regarding the configuration parameters of the user viewing interface in which the 3D viewing construct is to the rendered).
As per claim 3, most of the limitations of this claim have been noted in the rejection of claim 1 above. In addition, Fernandez disclose, wherein the type of analysis is automatically determined based on the one or more user-selected data fields (Fernandez: paragraph 0054: disclose user viewing interface receives user input from the user and transmits different types of user input to the user interface configuration module and navigation module, respectively. For example, the user input may include input regarding the configuration parameters of the user viewing interface in which the 3D viewing construct is to the rendered).
As per claim 4, most of the limitations of this claim have been noted in the rejection of claim 1 above. In addition, Fernandez disclose, wherein the plurality of selectable types of analysis include at least one of a group consisting of measure analysis, change analysis, category analysis, and distribution analysis (Fernandez: paragraph 0054: disclose user viewing interface receives user input from the user and transmits different types of user input to the user interface configuration module and navigation module, respectively. For example, the user input may include input regarding the configuration parameters of the user viewing interface in which the 3D viewing construct is to the rendered).
As per claim 5, most of the limitations of this claim have been noted in the rejection of claim 1 above. In addition, Fernandez disclose, detecting a user input selecting a data visualization of the subset of the plurality of candidate data visualizations; and in response to the user input selecting the data visualization, adding the selected data visualization to a dashboard of at least one visualization, wherein the dashboard is displayed in the user interface via display generation component (Fernandez: paragraph 0046 & Fig. 4: disclose a user interaction, such as rotating the display of the user device, may cause the re-rendering of the same 3D viewing construct in an updated user viewing interface. Examiner equates this teaching to subset of the plurality of candidate data visualizations).
As per claim 6, most of the limitations of this claim have been noted in the rejection of claim 1 above. In addition, Fernandez disclose, wherein the plurality of candidate data visualizations are generated based on additional data fields other than the one or more user-selected data fields (Fernandez: paragraph 0046 & Fig. 4: disclose a user interaction, such as rotating the display of the user device, may cause the re-rendering of the same 3D viewing construct in an updated user viewing interface and Fig. 4 Element 408, Element 404: disclose additional fields such as Advertisements and Supplemental Content Display panel).
As per claim 7, most of the limitations of this claim have been noted in the rejection of claims 1 and 6 above.
It is noted, however, Fernandez did not specifically detail the aspects of
wherein the additional data fields other than the one or more user- selected data fields are determined based on statistical analysis of the additional data fields meeting a variance threshold as recited in claim 7.
On the other hand, Anand achieved the aforementioned limitations by providing mechanisms of
wherein the additional data fields other than the one or more user- selected data fields are determined based on statistical analysis of the additional data fields meeting a variance threshold (Anand: paragraph 0090: disclose umber of user selected data fields exceeds some threshold. Examiner argues that the prior art teaches threshold and the statistical analysis is just a threshold).
As per claim 8, most of the limitations of this claim have been noted in the rejection of claim 1 above.
It is noted, however, Fernandez did not specifically detail the aspects of
wherein the plurality of candidate data visualizations include values from at least one of the one or more user-selected data fields as recited in claim 8.
On the other hand, Anand achieved the aforementioned limitations by providing mechanisms of
wherein the plurality of candidate data visualizations include values from at least one of the one or more user-selected data fields (Anand: Fig. 4 Element 406 and Element 408: disclose data visualizations).
As per claim 9, most of the limitations of this claim have been noted in the rejection of claim 1 above.
It is noted, however, Fernandez did not specifically detail the aspects of
wherein the plurality of candidate data visualizations are generated via a predictive model that associates the one or more user-selected data fields to the type of analysis as recited in claim 9.
On the other hand, Anand achieved the aforementioned limitations by providing mechanisms of
wherein the plurality of candidate data visualizations are generated via a predictive model that associates the one or more user-selected data fields to the type of analysis (Anand: paragraph 0064: disclose visual data analysis when they require scroll bars to fit on a display device. Examiner argues that predictive model is an algorithmic model that is applied).
As per claim 10, most of the limitations of this claim have been noted in the rejection of claim 1 above.
It is noted, however, Fernandez did not specifically detail the aspects of
wherein: the one or more user-selected data fields are selected from a plurality of data fields in the data source; and a respective data field of the plurality of data fields in the data source includes a respective type, including a quantitative measure, a categorical field, a temporal field, or a geographic field as recited in claim 10.
On the other hand, Anand achieved the aforementioned limitations by providing mechanisms of
wherein: the one or more user-selected data fields are selected from a plurality of data fields in the data source; and a respective data field of the plurality of data fields in the data source includes a respective type, including a quantitative measure, a categorical field, a temporal field, or a geographic field (Anand: paragraph 0088: disclose set of data fields contains a geographic field).
As per claim 11, most of the limitations of this claim have been noted in the rejection of claims 1 and 10 above.
It is noted, however, Fernandez did not specifically detail the aspects of
determining, based on the types of the one or more user-selected data fields, a secondary type of analysis from a plurality of selectable types of secondary analysis, wherein each secondary type of analysis is based on a combination of types of data attributes; and ranking, based on the secondary type of analysis, the plurality of candidate data visualizations as recited in claim 11.
On the other hand, Anand achieved the aforementioned limitations by providing mechanisms of
determining, based on the types of the one or more user-selected data fields, a secondary type of analysis from a plurality of selectable types of secondary analysis, wherein each secondary type of analysis is based on a combination of types of data attributes; and ranking, based on the secondary type of analysis, the plurality of candidate data visualizations (Anand: paragraph 0021: disclose ranking criterion scores each respective data visualization according to the view type of the respective data visualization and the user-selected data fields).
As per claim 12, most of the limitations of this claim have been noted in the rejection of claims 1, 10 and 11 above.
It is noted, however, Fernandez did not specifically detail the aspects of
the plurality of candidate data visualizations are associated with respective scores; the respective scores are based on the one or more user-selected data fields, the respective type of analysis, or the respective secondary type of analysis; and displaying the subset of the plurality of candidate data visualizations is based on the respective scores as recited in claim 12.
On the other hand, Anand achieved the aforementioned limitations by providing mechanisms of
the plurality of candidate data visualizations are associated with respective scores; the respective scores are based on the one or more user-selected data fields, the respective type of analysis, or the respective secondary type of analysis; and displaying the subset of the plurality of candidate data visualizations is based on the respective scores (Anand: paragraph 0022: disclose computed scores of the data visualizations and the computed scores of the alternative data visualizations).
As per claim 13, most of the limitations of this claim have been noted in the rejection of claims 1, 10 , 11 and 12 above.
It is noted, however, Fernandez did not specifically detail the aspects of
updating respective ranking of the subset of the plurality of candidate data visualizations based on the user selection of the subset of the subset of the plurality of candidate data visualizations as recited in claim 13.
On the other hand, Anand achieved the aforementioned limitations by providing mechanisms of
updating respective ranking of the subset of the plurality of candidate data visualizations based on the user selection of the subset of the subset of the plurality of candidate data visualizations (Anand: paragraph 0115: disclose ranking criteria is updated over time by the analytic module).
As per claim 14, most of the limitations of this claim have been noted in the rejection of claims 1, 10 , 11 and 12 above.
It is noted, however, Fernandez did not specifically detail the aspects of
updating respective ranking of the one or more data visualizations in the respective collection of the one or more collections based on the user selection of the subset of the one or more data visualizations in the respective collection of the one or more collections as recited in claim 14.
On the other hand, Anand achieved the aforementioned limitations by providing mechanisms of
updating respective ranking of the one or more data visualizations in the respective collection of the one or more collections based on the user selection of the subset of the one or more data visualizations in the respective collection of the one or more collections (Anand: Fig. 4 Element 406 and Element 408: disclose data visualizations).
As per claim 15, Fernandez disclose, A non-transitory computer readable storage medium storing one or more programs (Fernandez: paragraph 0074: disclose machine readable medium. Tangible non-transitory “storage”), the one or more programs comprising instructions, which when executed by an electronic device with a display generation component, and a touch-sensitive surface (Fernandez: paragraph 0070: disclose a smart phone, tablet), cause the electronic device to: remaining limitations in this claim 15 are similar to the limitations in claims 1, 3, 4 and 5. Therefore, examiner rejects these remaining limitations under the same rationale as limitations rejected under claims 1, 3, 4 and 5.
As per claim 16, Fernandez disclose, A computer system for generating data visualizations, comprising:
a display generation component;
one or more processors (Fernandez: paragraph 0073: disclose one or more processors); and
memory (Fernandez: paragraph 0073: disclose random access memory);
wherein the memory stores one or more programs configured for execution by the one or more processors, and the one or more programs comprising instructions (Fernandez: paragraph 0073: disclose possibly program instructions to be executed by the CPU) for: remaining limitations in this claim 16 are similar to the limitations in claim 15. Therefore, examiner rejects these remaining limitations under the same rationale as limitations rejected under claim 15.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US Pub. US 2016/0357829 A1 disclose “MULTI-FACTOR PRIORITIZATION AND VISUALIZATION TOOL”
US Pub. US 2016/0012129 A1 disclose “Computer-implemented method for visualizing data in report, involves receiving visualized data provided with profile data associated with data connection, where profile data is provided with metadata for describing contextual information”
Any inquiry concerning this communication or earlier communications from the examiner should be directed to PAVAN MAMILLAPALLI whose telephone number is (571)270-3836. The examiner can normally be reached on M-F. 8am - 4pm, EST.
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/PAVAN MAMILLAPALLI/
Primary Examiner, Art Unit 2159